APSTA-GE 2003: Intermediate Quantitative Methods

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# APSTA-GE 2003: Intermediate Quantitative Methods
## Lab Section 003, Week 12
### New York University
### 11/24/2020

---

## Reminders

- Assignment 7 ([group project](https://drive.google.com/drive/folders/1S0XC8s6-N4OM4X158DmAWjpxtCl_sVRg?usp=sharing))
  - [Group assignment](https://docs.google.com/spreadsheets/d/1uZ_ao7dts_wiipddpM9wz_JSYWfBFvcvZ5r6wvqkjTs/edit?usp=sharing)
  - Due: 12/08/2020 (Friday) 5:00 PM
  - Sample solutions will be available after due

- Office hours
  - Monday 9:00 - 10:00am (EST)
  - Wednesday 12:30 - 1:30pm (EST)
  - Additional time slots available
    - Sign-up sheet [HERE](https://docs.google.com/spreadsheets/d/1YY38yj8uCNIm1E7jaI9TJC494Pye2-Blq9eSK_eh6tI/edit?usp=sharing)
  - Office hour Zoom link [HERE](https://nyu.zoom.us/j/9985119253)

---

## Today's Topics

- Predict student' math scores
- Model Selection with Step-wise AIC

---

## The dataset

**Student Performance Data Set**

**Source**:

[Machine Learning Repository, UCI](https://archive.ics.uci.edu/ml/datasets/Student+Performance#)

Paulo Cortez, University of Minho, GuimarÃ£es, Portugal, http://www3.dsi.uminho.pt/pcortez

```r
dat <- read.csv("../student-mat.csv", sep = ";", header = TRUE)
dim(dat)
```

```
## [1] 395  33
```

```r
table(is.na(dat))
```

```
## 
## FALSE 
## 13035
```

---

## Variables

```r
colnames(dat)
```

```
##  [1] "school"     "sex"        "age"        "address"    "famsize"   
##  [6] "Pstatus"    "Medu"       "Fedu"       "Mjob"       "Fjob"      
## [11] "reason"     "guardian"   "traveltime" "studytime"  "failures"  
## [16] "schoolsup"  "famsup"     "paid"       "activities" "nursery"   
## [21] "higher"     "internet"   "romantic"   "famrel"     "freetime"  
## [26] "goout"      "Dalc"       "Walc"       "health"     "absences"  
## [31] "G1"         "G2"         "G3"
```

Label: `G3`

Features: Everything else

---

## Plan

1. EDA

2. Fit regression models

3. Evaluate model bias and variance

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## Prediction

The goal of prediction using regression is to fit a model that has the lowest AIC score.

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## Bias-variance tradeoff

[Bias-variance tradeoff](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff)

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## Contact

Tong Jin

- Email: tj1061@nyu.edu
- Office Hours
  - Mondays, 9 - 10am (EST)
  - Wednesdays, 12:30 - 1:30pm (EST)